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‎README.md‎

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# DCGAN
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### Title
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[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434)
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### Abstract
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In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks - demonstrating their applicability as general image representations.
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## Train
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$ python main.py --mode train \
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--scope [scope name] \
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--name_data [data name] \
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--dir_data [data directory] \
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--dir_log [log directory] \
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--dir_checkpoint [checkpoint directory]
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---
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$ python main.py --mode train \
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--scope dcgan \
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--name_data celeba \
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--dir_data ./datasets \
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--dir_log ./log \
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--dir_checkpoint ./checkpoint
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* Set **[scope name]** uniquely.
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* Hyperparameters were written to **arg.txt** under the **[log directory]**.
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* To understand hierarchy of directories based on their arguments, see **directories structure** below.
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## Test
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$ python main.py --mode test \
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--scope [scope name] \
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--name_data [data name] \
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--dir_data [data directory] \
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--dir_log [log directory] \
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--dir_checkpoint [checkpoint directory] \
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--dir_result [result directory]
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---
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$ python main.py --mode test \
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--scope dcgan \
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--name_data celeba \
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--dir_data ./datasets \
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--dir_log ./log \
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--dir_checkpoint ./checkpoints \
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--dir_result ./results
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* To test using trained network, set **[scope name]** defined in the **train** phase.
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* Generated images are saved in the **images** subfolder along with **[result directory]** folder.
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* **index.html** is also generated to display the generated images.
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## Tensorboard
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$ tensorboard --logdir [log directory]/[scope name]/[data name] \
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--port [(optional) 4 digit port number]
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---
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$ tensorboard --logdir ./log/dcgan/celeba \
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--port 6006
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After the above comment executes, go **http://localhost:6006**
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* You can change **[(optional) 4 digit port number]**.
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* Default 4 digit port number is **6006**.
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## Results
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![alt text](./img/generated_images.png "Generated Images by DCGAN")
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* The results were generated by a network trained with **celeba** dataset during **10 epochs**.
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* After the Test phase runs, execute **display_result.py** to display the figure.
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## Directories structure
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pytorch-DCGAN
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+---[dir_checkpoint]
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| \---[scope]
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| \---[name_data]
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| +---model_epoch00000.pth
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| | ...
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| \---model_epoch12345.pth
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+---[dir_data]
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| \---[name_data]
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| +---000000.png
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| | ...
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| \---12345.png
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+---[dir_log]
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| \---[scope]
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| \---[name_data]
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| +---arg.txt
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| \---events.out.tfevents
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\---[dir_result]
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\---[scope]
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\---[name_data]
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+---images
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| +---00000-output.png
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| | ...
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| +---12345-output.png
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\---index.html
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---
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pytorch-DCGAN
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+---checkpoints
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| \---dcgan
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| \---celeba
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| +---model_epoch00001.pth
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| | ...
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| \---model_epoch0010.pth
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+---datasets
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| \---celeba
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| +---000001.jpg
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| | ...
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| \---202599.jpg
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+---log
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| \---dcgan
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| \---celeba
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| +---arg.txt
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| \---events.out.tfevents
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\---results
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\---dcgan
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\---celeba
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+---images
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| +---0000-output.png
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| | ...
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| +---0127-output.png
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\---index.html
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* Above directory is created by setting arguments when **main.py** is executed.
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‎XCAT512.mat‎

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Binary file not shown.

‎backprojection.m‎

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function pdX = backprojection(pdY, param, bfig)
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if nargin < 3
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bfig = false;
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end
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if bfig
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figure('name', 'backprojection'); colormap gray;
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end
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pdX = zeros(param.nImgY, param.nImgX, 'like', pdY);
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dCurX = zeros(param.nImgY, param.nImgX, 'like', pdY);
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for iview = 0:param.nView-1
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dBeta = -iview*param.dView;
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dCurX(:) = 0;
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for iimgx = 0:param.nImgX-1
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dPosImgX = id2pos(iimgx + param.dOffsetImgX, param.dImgX, param.nImgX);
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for iimgy = 0:param.nImgY-1
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dPosImgY = id2pos(iimgy + param.dOffsetImgY, param.dImgY, param.nImgY);
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dRadius = sqrt(dPosImgY*dPosImgY + dPosImgX*dPosImgX);
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dPhi = atan2d(dPosImgY, dPosImgX);
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dDistY = dRadius*cosd(dBeta - dPhi);
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nCurIdDctX = pos2id(dDistY, param.dDctX, param.nDctX) - param.dOffsetDctX + 1;
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if (nCurIdDctX <= 1 || nCurIdDctX >= param.nDctX)
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continue;
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end
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nPreIdDctX = floor(nCurIdDctX);
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nPostIdDctX = nPreIdDctX + 1;
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dCurX_ = interpolation1d(pdY(:, iview + 1), nCurIdDctX, nPreIdDctX, nPostIdDctX);
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dCurX(iimgy + 1, iimgx + 1) = dCurX_;
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end
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end
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pdX = pdX + dCurX;
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if bfig
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imagesc(pdX); title([num2str(iview + 1) ' / ' num2str(param.nView)]);
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xlabel('x-axis'); ylable('y-axis');
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drawnow();
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end
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end
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end

‎demo_parallelbeam_ct.m‎

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clear ;
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close all;
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%%
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restoredefaultpath();
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addpath('./lib');
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%% Setting the CT System parameters
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% dAngle: Measure from 0 until the angle
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% nView : # of the views [unit]
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% dView : Gap between view_(k) - view_(k-1) [degree]
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param.dAngle = 180;
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param.nView = 90; % # of elements
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param.dView = param.dAngle/param.nView; % degree
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param.pdView = linspace(0, param.dAngle, param.nView + 1);
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param.pdView(end) = [];
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% X-ray source
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% DSO : Distance from the Source to the Object [mm]
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% DSD : Distance from the Source to the Detector [mm]
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param.DSO = 400 ; % mm
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param.DSD = 800; % mm
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% X-ray detector
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param.dDctX = 1; % mm
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param.nDctX = 185; % # of elements
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param.dOffsetDctX = 0; % # of elements
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param.compute_filtering = 'fft'; % method for computing the filtering function : 'conv', 'fft'
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% Object size
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param.dImgY = 1; % mm
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param.dImgX = 1; % mm
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param.nImgY = 128; % # of elements
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param.nImgX = 128; % # of elements
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param.dOffsetImgX = 25; % # of elements
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param.dOffsetImgY = 25; % # of elements
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%%
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load('XCAT512.mat');
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src = imresize(XCAT512, [param.nImgY, param.nImgX]);
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%%
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disp ('built-in ver.');
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disp ('projection - built-in');
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tic;
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prj_matlab = radon(src, param.pdView);
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toc;
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disp ('filtering - built-in');
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tic;
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prj_matlab_flt = filterProjections(prj_matlab, 'ram-lak', 1);
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toc;
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disp ('backprojection - built-in');
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tic;
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dst_matlab = iradon(prj_matlab_flt, param.pdView, 'none', param.nImgY);
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toc;
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%%
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disp ('implementation ver.')
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disp ('projection - implementation');
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tic;
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prj_imp = projection(src, param);
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toc;
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disp ('filtering - implementation');
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tic;
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prj_imp_flt = filtering(prj_imp, param);
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toc;
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disp ('backprojection - implementation');
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tic;
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dst_imp = backprojection(prj_imp_flt, param);
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toc;
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%%
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wndImg = [0, max(src(:))];
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figure(2); colormap gray;
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subplot(331); imagesc(src, wndImg);
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axis image; xlabel('X-axis'); ylabel('Y-axis'); title('ground truth');
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subplot(332); imagesc(dst_matlab, wndImg);
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axis image; xlabel('X-axis'); ylabel('Y-axis'); title(['reconstruction_{built-in, # of view = ' num2str(param.nView) '}']);
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subplot(333); imagesc(dst_imp, wndImg);
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axis image; xlabel('X-axis'); ylabel('Y-axis'); title(['reconstruction_{implementation, # of view = ' num2str(param.nView) '}']);
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subplot(335); imagesc(src - dst_matlab);
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axis image; xlabel('X-axis'); ylabel('Y-axis'); title(['difference_{ground truth - built-in, # of view = ' num2str(param.nView) '}']);
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subplot(336); imagesc(src - dst_imp);
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axis image; xlabel('X-axis'); ylabel('Y-axis'); title(['difference_{ground truth - implementation, # of view = ' num2str(param.nView) '}']);
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subplot(338); imagesc(prj_matlab);
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xlabel(['Angle ( \Delta\theta : ' num2str(param.dView) ' \circ )']); ylabel('Detector'); title(['sinogram_{built-in, # of view = ' num2str(param.nView) '}']);
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ax = gca;
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ax.XTick = linspace(0, param.nView, 10);
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ax.XTickLabel = linspace(0, param.dAngle, 10);
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subplot(339); imagesc(prj_imp);
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xlabel(['Angle ( \Delta\theta : ' num2str(param.dView) ' \circ )']); ylabel('Detector'); title(['sinogram_{implementation, # of view = ' num2str(param.nView) '}']);
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ax = gca;
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ax.XTick = linspace(0, param.nView, 10);
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ax.XTickLabel = linspace(0, param.dAngle, 10);

‎filtering.m‎

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function [pdFltY, pdFlt] = filtering(pdY, param)
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pdFltY = zeros(param.nDctX, param.nView);
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pdFlt = generate_filter(param.dDctX, param.nDctX);
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switch param.compute_filtering
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case 'conv'
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%% CONVOLUTION ver.
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for iview = 0:param.nView-1
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pdFltY(:, iview+1) = convolution1d(pdY(:, iview+1), pdFlt, param.nDctX);
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end
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case 'fft'
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%% FFT ver.
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pdFltY_ = zeros(param.nDctX, 1);
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% nFlt = max(64,2^nextpow2(2*length(pdFlt)));
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nFlt = length(pdFlt);
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pdFlt_ = pdFlt;
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pdFlt_(nFlt) = 0;
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pdFlt_ = fft(pdFlt_);
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for iview = 0:param.nView-1
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pdFltY_(:) = 0;
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pdY_ = pdY(:, iview+1);
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pdY_(nFlt) = 0;
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pdY_ = fft(pdY_);
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for iflt = 0:nFlt-1
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pdFltY_(iflt+1) = pdFlt_(iflt+1)*pdY_(iflt+1);
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end
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pdFltY_ = ifft(pdFltY_, 'symmetric');
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pdFltY(:, iview+1) = pdFltY_(nFlt - param.nDctX + 1: nFlt);
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end
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end
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%%
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pdFltY = pi/param.nView*param.dDctX*pdFltY;
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end
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‎lib/convolution1d.m‎

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function y = convolution1d(x, ker, n)
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y = zeros(n, 1);
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for i = 0:n-1
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for j = 0:n-1
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y(i+1) = y(i+1) + ker(((n-1) + i+1) - (j+1) + 1)*x(j+1);
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end
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end
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end

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